Light weight Micro-Aerial Vehicles (MAVs) equipped with sensors can autonomously access environments that are difficult to access for ground robots. Due to this capability, MAVs have become popular in many robot missions, e.g., structure inspection, environment mapping, reconnaissance and large-scale data gathering. Compared with ground robots, there are two main challenges for MAV autonomous navigation: (1) limited payload, power and onboard computing resources, allowing only light-weight compact sensors (like cameras) to be integrated for MAV applications; and (2) MAVs usually move with fast and aggressive six degrees of freedom (DoF) motions. Accordingly, their state estimation, environment perception and obstacle avoidance are more difficult than ground robots.
For aerial vehicles, and, in particular, for micro-aerial vehicles (MAVs), state estimation is the most critical capability for localization, autonomous obstacle avoidance, robust flight control and 3D environmental mapping. There are three main challenges for a MAV state estimator: (1) it must be able to deal with aggressive 6 degree of freedom (DoF) motion; (2) it should be robust to intermittent global positioning system (GPS) situations, and even to GPS-denied situations; and (3) it should work well both for low- and high-altitude flight.
Robust, accurate and smooth high-rate state estimation is the most critical capability to realize truly autonomous flight of MAVs. The state estimator reports the six DoF MAV position, orientation and the velocity, so the output of the estimator serves as the input for environment mapping, motion planning and trajectory-following control. Global positioning system (GPS) combined with the inertial measurement unit (IMU) state estimation technique has been widely utilized for providing MAV high-rate state information. However, applications of low-rate GPS are limited to open environments. Furthermore, GPS cannot provide accurate positioning information for MAVs, especially in terms of altitude.
As a complimentary sensor for GPS, the IMU measures tri-axis accelerations and rotation rates in the IMU body frame, and the velocity and orientation are calculated as the integral of accelerations and rotation rates over time. For low-cost commercial IMUs, the inertia integral will drift very fast without global rectification information. As a result, the integration of additional sensing is a way to further improve state estimation redundancy, accuracy and robustness.
Because of the low cost, low energy consumption and satisfactory accuracy, camera-based visual odometry (VO) is an ideal choice for providing additional measurements. Stereo visual sensors reconstruct the environment features with the metric scale from the stereo baseline, so stereo-based VO easily generates six DoF pose measurements. The performance of stereo VO highly depends on the ratio between the stereo baseline and environmental depth, namely the baseline-depth ratio. The depth standard deviation from stereo is proportional to the quadratic of depth; thus, stereo VO is limited to short range measurements. At stereo disparities lower than 10 pixels, the depth triangulation from a single stereo rig tends to follow a non-Gaussian curve with a long tail. For cases with a large baseline-depth ratio (e.g., MAV high-altitude flights), the stereo effectively degenerates to a monocular system, thus losing the capability of pose measurements.